Vehicle lane keeping system based on TSK fuzzy extension control

Wang, Hongbo; Cui, Wei; Xia, Zhi; Jiang, Wuhua

doi:10.1177/0954407019849257

Cited by 14 publications

(13 citation statements)

References 20 publications

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“…Therefore, the safety of automobiles is an important research point in front of people. According to the World Health Organization, traffic accidents have become the second leading cause of death among young people and the third leading cause of death among young and middle-aged people in the world [ 1 ]. The assisted driving system is such an active safety system, which can monitor road information and driving conditions in real time, can automatically identify traffic warning signs for prompting or warning, can determine whether the distance to the traffic ahead is within the safe range, whether there is a possibility of collision, can determine whether the local cars or pedestrians are within the safe range, and can determine whether the driver is driving in fatigue.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment

Yan

2022

Computational Intelligence and Neuroscience

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In this paper, we propose an assisted driving system implemented with a Jetson nano-high-performance embedded platform by using machine vision and deep learning technologies. The vehicle dynamics model is established under multiconditional assumptions, the path planner and path tracking controller are designed based on the model predictive control algorithm, and the local desired path is reasonably planned in combination with the behavioral decision system. The behavioral decision algorithm based on finite state machine reasonably transforms the driving state according to the environmental changes, realizes the following of the target vehicle speed, and can take effective emergency braking in time when there is a collision danger. The system can complete the motion planning by the model predictive control algorithm and control the autonomous vehicle to smoothly track the replanned local desired path to complete the lane change overtaking action, which can meet the demand of ADAS. The path planner is designed based on the MPC algorithm, solving the objective function with obstacle avoidance function, planning the optimal path that can avoid a collision, and using 5th order polynomial to fit the output local desired path points. In 5∼8 s time, the target vehicle decelerates to 48 km/h; the autonomous vehicle immediately makes a deceleration action and gradually reduces the speed difference between the two vehicles until it reaches the target speed, at which time the distance between the two vehicles is close to the safe distance, obtained by the simulation test results. The system can still accurately track the target when the vehicle is driving on a curve and timely control the desired speed change of the vehicle, and the target vehicle always maintains a safe distance. The system can be used within 50 meters.

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Section: Introductionmentioning

confidence: 99%

Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment

Yan

2022

Computational Intelligence and Neuroscience

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“…(3) For lane keeping behavior, combining previous studies [ 35 , 36 , 37 ] and the specific situation of this study, the period where

was extracted first, then the extracted segments were further verified by outside ambient videos.…”

Section: Methodology Descriptionmentioning

confidence: 99%

Real-Time Driving Behavior Identification Based on Multi-Source Data Fusion

Xie

Chen

et al. 2021

IJERPH

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Real-time driving behavior identification has a wide range of applications in monitoring driver states and predicting driving risks. In contrast to the traditional approaches that were mostly based on a single data source with poor identification capabilities, this paper innovatively integrates driver expression into driving behavior identification. First, 12-day online car-hailing driving data were collected in a non-intrusive manner. Then, with vehicle kinematic data and driver expression data as inputs, a stacked Long Short-Term Memory (S-LSTM) network was constructed to identify five kinds of driving behaviors, namely, lane keeping, acceleration, deceleration, turning, and lane changing. The Artificial Neural Network (ANN) and XGBoost algorithms were also employed as a comparison. Additionally, ten sliding time windows of different lengths were introduced to generate driving behavior identification samples. The results show that, using all sources of data yields better results than using the kinematic data only, with the average F1 value improved by 0.041, while the S-LSTM algorithm is better than the ANN and XGBoost algorithms. Furthermore, the optimal time window length is 3.5 s, with an average F1 of 0.877. This study provides an effective method for real-time driving behavior identification, and thereby supports the driving pattern analysis and Advanced Driving Assistance System.

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“…Benloucif et al [23] considered driving freedom in a lane keeping task and proposed a cooperative trajectory-planning method according to driver actions and intentions. Wang et al [24] developed a novel lane keeping system, and a single-point preview model is utilized to describe driver steering behavior. Sentouh et al [25] introduced a control authority allocation method based on energy spent by the driver, driver satisfaction level, and contradiction level between the driver and autonomous controller.…”

Section: Introductionmentioning

confidence: 99%

Human–Machine Cooperative Control of Intelligent Vehicles for Lane Keeping—Considering Safety of the Intended Functionality

et al. 2021

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Reasonably foreseeable misuse by persons, as a primary aspect of safety of the intended functionality (SOTIF), has a significant effect on cooperation performance for lane keeping. This paper presents a novel human–machine cooperative control scheme with consideration of SOTIF issues caused by driver error. It is challenging to balance lane keeping performance and driving freedom when driver error occurs. A safety evaluation strategy is proposed for safety supervision, containing assessments of driver error and lane departure risk caused by driver error. A dynamic evaluation model of driver error is designed based on a typical driver model in the loop to deal with the uncertainty and variability of driver behavior. Additionally, an extension model is established for determining the cooperation domain. Then, an authority allocation strategy is proposed to generate a dynamic shared authority and achieve an adequate balance between lane keeping performance and driving freedom. Finally, a model predictive control (MPC)-based controller is designed for calculating optimal steering angle, and a steer-by-wheel (SBW) system is employed as an actuator. Numerical simulation tests are conducted on driver error scenarios based on the CarSim and MATLAB/Simulink software platforms. The simulation results demonstrate the effectiveness of the proposed method.

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Vehicle lane keeping system based on TSK fuzzy extension control

Cited by 14 publications

References 20 publications

Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment

Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment

Real-Time Driving Behavior Identification Based on Multi-Source Data Fusion

Human–Machine Cooperative Control of Intelligent Vehicles for Lane Keeping—Considering Safety of the Intended Functionality

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